Supramolecular Lipid Nanoparticles Based on Amine β-CD Host–Guest Lipids: Design, Mechanisms, and Biosafety
Bibliographic record
Abstract
Background/Objectives: Lipid nanoparticles (LNPs) have demonstrated notable clinical success as advanced drug delivery systems. However, the development of novel covalently bonded ionizable lipids faces substantial technical challenges, as their modification is difficult and they have a high molecular weight. To address this issue, we report the use of host–guest complexes in supramolecular chemistry as functional lipid motifs for constructing LNPs. Methods: Ionizable amine β-cyclodextrin (amine β-CD)-derived host–guest amphiphilic lipid molecules (HGLs) were designed for the construction of multi-stage assembly supramolecular LNPs (MSLNPs). The structure–function relationships and stability of MSLNPs were explored by screening eight types of amine β-CDs and varying the ratio of HGL to yolk phosphatidylcholine. Stability screening and molecular dynamics simulations were performed to clarify the self-assembly mechanisms and optimal formulations, followed by a systematic evaluation of delivery performance. Results: MSLNPs showed a high drug-loading efficiency (> 30%), a rapid-response release in acidic environments, and multi-pathway cellular uptake. In vivo delivery experiments using ethylenediamine β-CD-based MSLNPs in mice revealed no significant immunogenicity, no significant abnormalities in organs/tissues or their functions, a unique biodistribution pattern, and pronounced renal targeting. The successful development of MSLNPs with acidic pH-responsive control, a high delivery efficiency, and renal-targeting properties simplifies LNP preparation. Conclusions: This study offers novel insights into the design of simplified LNPs and the optimization of targeted delivery, with potential applications in renal disease therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".